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A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets [article]

Fabio S. Ferreira, Agoston Mihalik, Rick A. Adams, John Ashburner, Janaina Mourao-Miranda
2021 arXiv   pre-print
Canonical Correlation Analysis (CCA) and its regularised versions have been widely used in the neuroimaging community to uncover multivariate associations between two data modalities (e.g., brain imaging  ...  Group Factor Analysis (GFA) is a hierarchical model that addresses the first two limitations by providing Bayesian inference and modelling modality-specific associations.  ...  CCA Canonical Correlation Analysis was introduced by Hotelling (1936) and is a classical method for seeking maximal correlations between linear combinations of two multivariate data sets, which can be  ... 
arXiv:2103.06845v1 fatcat:sjn3usfnz5b4fb6siwcot46v2e

A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets

Fabio S. Ferreira, Agoston Mihalik, Rick A. Adams, John Ashburner, Janaina Mourao-Miranda
2021 NeuroImage  
Canonical Correlation Analysis (CCA) and its regularised versions have been widely used in the neuroimaging community to uncover multivariate associations between two data modalities (e.g., brain imaging  ...  Group Factor Analysis (GFA) is a hierarchical model that addresses the first two limitations by providing Bayesian inference and modelling modality-specific associations.  ...  CCA Canonical Correlation Analysis was introduced by Hotelling (1936) and is a classical method for seeking maximal correlations between linear combinations of two multivariate data sets, which can be  ... 
doi:10.1016/j.neuroimage.2021.118854 pmid:34971767 pmcid:PMC8861855 fatcat:2mg7jhvnnzbabffcoaorjvj3kq

Minimax Quasi-Bayesian estimation in sparse canonical correlation analysis via a Rayleigh quotient function [article]

Qiuyun Zhu, Yves Atchade
2021 arXiv   pre-print
Canonical correlation analysis (CCA) is a popular statistical technique for exploring the relationship between datasets.  ...  The estimation of sparse canonical correlation vectors has emerged in recent years as an important but challenging variation of the CCA problem, with widespread applications.  ...  Model-based approaches to canonical correlation analysis were developed in the mid 2000's (see e.g., [5] ), and paved the way for a Bayesian treatment of canonical correlation analysis ( [6] ; [7] )  ... 
arXiv:2010.08627v2 fatcat:r42vna2ybfdkrlwexyb2rl3u2e

Integrating Heterogeneous omics Data via Statistical Inference and Learning Techniques

Ashar Ahmad, Holger Fröhlich
2016 Genomics and Computational Biology  
Multi-omics studies are believed to provide a more comprehensive picture of a complex biological system than traditional studies with one omics data source.  ...  Furthermore, the iCluster method can be seen as a special case of Bayesian canonical correlation analysis with a sparsity prior for the coefficient matrix [108] , facilitating model identifiability and  ...  Detecting these types of correlations is essentially the motivation behind canonical correlation analysis (CCA) [31] .  ... 
doi:10.18547/gcb.2016.vol2.iss1.e32 fatcat:xmdsdhzdj5czvljgfhvwqlypbm

Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO!

Natalia Vilor-Tejedor, Diego Garrido-Martín, Blanca Rodriguez-Fernandez, Sander Lamballais, Roderic Guigó, Juan Domingo Gispert
2021 Computational and Structural Biotechnology Journal  
In this article, we review novel methods and strategies focused on the analysis of multiple phenotypes and genetic data.  ...  Most studies focus on individual correlation and association tests between genetic variants and a single measurement of the brain.  ...  Legend: BGSMTR: Bayesian group sparse multi-task regression model; CCA: Canonical Correlation Analysis; GLM: General Linear Model; ICA: Independent Component Analysis; ICA-MFA: Independent Multiple Factor  ... 
doi:10.1016/j.csbj.2021.10.019 pmid:34765095 pmcid:PMC8567328 fatcat:qxpol6cydbfothoeeox4u3e7vq

Genetic and phenotypic associations between brain imaging, psychopathology and educational attainment in children aged 9-11 [article]

Sara Fernandez-Cabello, Dag Alnas, Dennis van der Meer, Andreas Dahl, Madelene Christin Holm, Rikka Kjelkenes, Ivan I. Maximov, Linn B. Norbom, Mads L. Pedersen, Irene Voldsbekk, Ole A. Andreassen, Lars T. Westlye
2022 medRxiv   pre-print
We then assessed the relationship of imaging components with genetic and clinical psychiatric risk with univariate models and Canonical correlation analysis (CCA).  ...  We used principal component analysis to derive imaging components, and calculated their heritability.  ...  values; 4) exclusion of participants with more than 10% of missing values in each imaging modality (structural and diffusion MRI) and 5) imputation of the remaining missing values with k-nearest neighbors  ... 
doi:10.1101/2022.02.01.22270003 fatcat:77wxaeyuw5ep7jh6fnvac5ay64

Computational strategies for single-cell multi-omics integration

Nigatu Adossa, Sofia Khan, Kalle T Rytkönen, Laura L Elo
2021 Computational and Structural Biotechnology Journal  
In this review, we first introduce recent developments in single-cell multi-omics in general and then focus on the available data integration strategies.  ...  To pair with the recent biotechnological developments, many computational approaches to process and analyze single-cell multi-omics data have been proposed.  ...  cells and features between two modalities to formulate the canonical correlation vectors in a latent space.  ... 
doi:10.1016/j.csbj.2021.04.060 pmid:34025945 pmcid:PMC8114078 fatcat:qap257yttzdetjrqs4aijcwaq4

Bayesian multi-modal model comparison: A case study on the generators of the spike and the wave in generalized spike–wave complexes

Jean Daunizeau, Anna E. Vaudano, Louis Lemieux
2010 NeuroImage  
We illustrate the method using EEG-correlated fMRI data acquired in a patient with ictal generalized spike-wave (GSW) discharges, to examine whether different networks are involved in the generation of  ...  We present a novel approach to assess the networks involved in the generation of spontaneous pathological brain activity based on multi-modal imaging data.  ...  To the best of our knowledge, this work demonstrates the first application of Bayesian multi-modal EEG-fMRI modelling to the fine spatio-temporal characterization of the neural correlates of generalized  ... 
doi:10.1016/j.neuroimage.2009.06.048 pmid:19559798 fatcat:kksj72oebbhbvlcwtt454ykmku

A Survey of Multi-View Representation Learning [article]

Yingming Li, Ming Yang, Zhongfei Zhang
2017 arXiv   pre-print
Representative examples are canonical correlation analysis (CCA) and its several extensions.  ...  Then from the perspective of representation fusion we investigate the advancement of multi-view representation learning that ranges from generative methods including multi-modal topic learning, multi-view  ...  [67] introduce robust canonical correlation analysis by replacing Gaussian distributions with Student-t distributions, which constructs mixtures of robust CCA and can deal with missing data quite easily  ... 
arXiv:1610.01206v4 fatcat:xsi7ufxnlbdk5lz6ykrsnexfvm

Data science in neurodegenerative disease

Sepehr Golriz Khatami, Sarah Mubeen, Martin Hofmann-Apitius
2020 Current Opinion in Neurology  
With the advancement of computational approaches and abundance of biomedical data, a broad range of neurodegenerative disease models have been developed.  ...  factorization, nonhierarchical cluster analysis, hierarchical agglomerative clustering, and deep-learning-based approaches have been employed to stratify patients based on their disease subtypes.  ...  Nonetheless, in NDD research, certain modalities, such as genetic [2, 3] and neuro-imaging [4] [5] [6] , are well-suited for unimodal biomarker analysis.  ... 
doi:10.1097/wco.0000000000000795 pmid:32073441 pmcid:PMC7077964 fatcat:deglg4ogszbj5if7b2pv3kw42y

Semi-supervised Bayesian Deep Multi-modal Emotion Recognition [article]

Changde Du, Changying Du, Jinpeng Li, Wei-long Zheng, Bao-liang Lu, Huiguang He
2017 arXiv   pre-print
In this paper, we first build a multi-view deep generative model to simulate the generative process of multi-modality emotional data.  ...  Compared with previous emotion recognition methods, our method is more robust and flexible.  ...  The Bayesian Canonical Correlation Analysis (CCA) model [Klami et al., 2013] can be seen as a special case of our model, where linear shallow transformations were used to generate each data view and  ... 
arXiv:1704.07548v1 fatcat:o334v3e6kvceplx5ouimcg4xzm

Multi-View Visual Recognition of Imperfect Testing Data

Qilin Zhang, Gang Hua
2015 Proceedings of the 23rd ACM international conference on Multimedia - MM '15  
The consensus information is preserved by projection matrices learned with modified canonical-correlation analysis (CCA) optimization terms with new, explicit classsimilarity constraints.  ...  To address these challenges, we choose the latent space model and introduce a new similarity learning canonical-correlation analysis (SLC-CA) method to capture the semantic consensus between views.  ...  This is achieved by a new similarity learning canonicalcorrelation analysis algorithm, namely, the similarity learning canonical-correlation analysis (SLCCA) algorithm, inspired by [34, 35] , which constructs  ... 
doi:10.1145/2733373.2806224 dblp:conf/mm/ZhangH15 fatcat:axhtvfczobgpbleofsdldtf7ya

Amodal Processing in Human Prefrontal Cortex

B. J. Tamber-Rosenau, P. E. Dux, M. N. Tombu, C. L. Asplund, R. Marois
2013 Journal of Neuroscience  
of multivariate pattern analysis in these regions.  ...  modality representation.  ...  Bayesian analysis of modality coding pooled across Experiments 1-4 and process coding in Experiment 5 for ROIs derived from canonical MD region coordinates .  ... 
doi:10.1523/jneurosci.4601-12.2013 pmid:23843526 pmcid:PMC3724542 fatcat:5vsch7fobzbcjp2fjzqifflw7a

Nonparametric bayesian upstream supervised multi-modal topic models

Renjie Liao, Jun Zhu, Zengchang Qin
2014 Proceedings of the 7th ACM international conference on Web search and data mining - WSDM '14  
Our model develops a compound nonparametric Bayesian multi-modal prior to describe the correlation structure of data both within each individual modality and between different modalities.  ...  Learning with multi-modal data is at the core of many multimedia applications, such as cross-modal retrieval and image annotation.  ...  Representative works of the first class include canonical correlation analysis (CCA) and its variants [31, 28] .  ... 
doi:10.1145/2556195.2556238 dblp:conf/wsdm/LiaoZQ14 fatcat:kcg6y7jbunezxbqb3ieeu44ecy

Using Multi-Scale Genetic, Neuroimaging and Clinical Data for Predicting Alzheimer's Disease and Reconstruction of Relevant Biological Mechanisms

Shashank Khanna, Daniel Domingo-Fernández, Anandhi Iyappan, Mohammad Asif Emon, Martin Hofmann-Apitius, Holger Fröhlich
2018 Scientific Reports  
It is a chronic disease that usually starts slowly with a pre-symptomatic phase and worsens over time 2 .  ...  In addition to the general limitation of such a purely simulation based validation the actually utility for clinical practice would have to be validated in  ...  In addition, we compared our approach against supervised sparse Generalized Canonical Correlation Analysis (ssGCCA) 18, 19 in conjunction with a conventional Cox regression as predictive model.  ... 
doi:10.1038/s41598-018-29433-3 pmid:30042519 pmcid:PMC6057884 fatcat:bo7fy35j5jcopg5unr2rdgaxey
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